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The Intelligence Graph

We are not reading
AI outputs.
We are correlating
what drives them.

Rankall.io maps the relationship between 60+ trust signals and AI recommendation behavior across 11 platforms. Every scan adds to that map. Every verified outcome makes it more accurate.

How the intelligence graph works

60+ Trust Signal Sources

Reviews & Ratings

30%
Google Business ProfileYelpTrustpilotTripadvisorBBB

Social Presence

20%
RedditFacebookTikTokYouTubeLinkedInX/Twitter

Directories

15%
HealthgradesAvvoG2ClutchZocdocMartindale

Schema & Technical

15%
LocalBusiness JSON-LDFAQ SchemaProduct SchemaPageSpeed

Citations & Press

10%
Google NewsLocal PressIndustry PublicationsAuthority Links

E-Commerce Signals

10%
AmazonEtsyGoogle ShoppingTikTok ShopShopify

Rankall Engine

Intelligence Engine

7-layer ML pipeline

1
Intent Classification
2
Signal Correlation
3
Gap Diagnosis
4
Fix Prediction
5
Content Generation
6
Outcome Verification
7
Model Retraining

11 AI Recommendation Platforms

ChatGPT

25%
FAQ SchemaAuthority PressStructured Data

Gemini

18%
Google BusinessReviewsGoogle News

Perplexity

11%
RedditCitationsCommunity Forums

Grok

8%
X/TwitterSocial MentionsTrending

Claude

7%
Structured DataAuthority Sources

Groq

3%
General Trust Signals

Kimi

4%
E-CommerceProduct Reviews

The compounding loop

Every scan, fix, and verified outcome makes the next prediction more accurate. The product gets more valuable over time.

01

Scan runs

All 11 AI platforms queried simultaneously. 60+ trust signals pulled in parallel.

02

Correlation mapped

ML engine correlates which signals drove which platform recommendations for this business type and location.

03

Fix predicted

Based on verified outcomes from similar businesses, fix impact is predicted before anything is applied.

04

Fix applied

Business applies the recommended fix using AI-generated content from the Fix Engine.

05

Outcome verified

Automated rescan runs in 30 days. Before and after scores compared. Timestamped proof stored.

06

Model improves

Verified outcome feeds back into the prediction model. Next similar business gets a more accurate prediction.

Loop repeats — model improves with every scan

Patterns the graph surfaces

These are consistent patterns we see emerging across our dataset — not programmed rules, but discovered correlations.

89%

FAQ schema correlation

Businesses with complete FAQ schema markup consistently appear in ChatGPT recommendations for service queries. Those without it rarely do.

~0.8

Reddit-Perplexity link

Reddit community presence shows a strong correlation with Perplexity local recommendations — stronger than most other individual signals we track.

+20pts

Review response impact

Businesses that actively respond to Google reviews consistently score higher on Gemini local queries across multiple categories and cities.

62%

Tripadvisor citation rate

Tripadvisor is cited in 62% of Austin restaurant queries across all platforms we track — making it the highest-leverage missing signal for restaurants in our dataset.

The longer view

The graph becomes more valuable the more it grows.

A product that shows you a score is useful today. A system that maps why AI recommends one business over another — and gets more accurate with every data point — becomes something more durable over time.

That is the direction we are building in. Not a dashboard. A correlation engine that gets smarter with every scan, every fix, and every verified outcome.

60+

Signal sources mapped

10

AI platforms correlated

7

Industries tracked

Day 1

Dataset building started

See the graph working
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